/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-17 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the
                    Eclipse Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */

/*
Old example for deprecated interface.
$spell
	CppAD
	Jac
$$

$section Computing Dependency: Example and Test$$

$head Discussion$$
The partial of an dependent variable with respect to an independent variable
might always be zero even though the dependent variable depends on the
value of the dependent variable. Consider the following case
$latex \[
f(x) = {\rm sign} (x) =
\left\{ \begin{array}{rl}
	+1 & {\rm if} \; x > 0 \\
	0  & {\rm if} \; x = 0 \\
	-1 & {\rm if} \; x < 0
\end{array} \right.
\] $$
In this case the value of $latex f(x)$$ depends on the value of $latex x$$
but CppAD always returns zero for the derivative of the $cref sign$$ function.

$head Dependency Pattern$$
If the $th i$$ dependent variables depends on the
value of the $th j$$ independent variable,
the corresponding entry in the dependency pattern is non-zero (true).
Otherwise it is zero (false).
CppAD uses $cref/sparsity patterns/glossary/Sparsity Pattern/$$
to represent dependency matrices.
The $icode dependency$$ argument to
$cref/ForSparseJac/ForSparseJac/dependency/$$ and
$cref/RevSparseJac/RevSparseJac/dependency/$$ is a flag that signals
that the dependency pattern (instead of the sparsity pattern) is computed.

$code
$srcfile%test_more/general/dependency.cpp%0%// BEGIN C++%// END C++%1%$$
$$

$end
*/
// BEGIN C++
# include <cppad/cppad.hpp>
namespace {
	double heavyside(const double& x)
	{	if( x <= 0.0 )
			return 0.0;
		return 1.0;
	}
	CPPAD_DISCRETE_FUNCTION(double, heavyside)
}

bool dependency(void)
{	bool ok = true;
	using CppAD::AD;
	using CppAD::NearEqual;

	// VecAD object for use later
	CppAD::VecAD<double> vec_ad(2);
	vec_ad[0] = 0.0;
	vec_ad[1] = 1.0;

	// domain space vector
	size_t n  = 5;
	CPPAD_TESTVECTOR(AD<double>) ax(n);
	for(size_t j = 0; j < n; j++)
		ax[j] = AD<double>(j + 1);

	// declare independent variables and start tape recording
	CppAD::Independent(ax);

	// some AD constants
	AD<double> azero(0.0), aone(1.0);

	// range space vector
	size_t m  = n;
	size_t m1 = n - 1;
	CPPAD_TESTVECTOR(AD<double>) ay(m);
	ay[m1-0] = sign( ax[0] );
	ay[m1-1] = CondExpLe( ax[1], azero, azero, aone);
	ay[m1-2] = CondExpLe( azero, ax[2], azero, aone);
	ay[m1-3] = heavyside( ax[3] );
	ay[m1-4] = vec_ad[ ax[4] - AD<double>(4.0) ];

	// create f: x -> y and stop tape recording
	CppAD::ADFun<double> f(ax, ay);

	// -----------------------------------------------------------
	// ForSparseJac and bool dependency
	bool transpose  = false;
	bool dependency;
	// could replace CppAD::vectorBooll by CPPAD_TEST_VECTOR<bool>
	CppAD::vectorBool eye_bool(n * n), depend_bool(m * n);
	for(size_t i = 0; i < n; i++)
	{	for(size_t j = 0; j < n; j++)
			eye_bool[i * n + j] = (i == j);
	}
	dependency = true;
	depend_bool = f.ForSparseJac(n, eye_bool, transpose, dependency);
	for(size_t i = 0; i < m; i++)
	{	for(size_t j = 0; j < n; j++)
			ok &= depend_bool[i * n + j] == (i == (m1-j));
	}
	dependency = false;
	depend_bool = f.ForSparseJac(n, eye_bool, transpose, dependency);
	for(size_t i = 0; i < m; i++)
	{	for(size_t j = 0; j < n; j++)
			ok &= depend_bool[i * n + j] == false;
	}

	// -----------------------------------------------------------
	// RevSparseJac and set dependency
	CppAD::vector<    std::set<size_t> > eye_set(m), depend_set(m);
	for(size_t i = 0; i < m; i++)
	{	ok &= eye_set[i].empty();
		eye_set[i].insert(i);
	}
	dependency = true;
	depend_set = f.RevSparseJac(n, eye_set, transpose, dependency);
	for(size_t i = 0; i < m; i++)
	{	std::set<size_t> check;
		check.insert(m1 - i);
		ok &= depend_set[i] == check;
	}
	dependency = false;
	depend_set = f.RevSparseJac(n, eye_set, transpose, dependency);
	for(size_t i = 0; i < m; i++)
		ok &= depend_set[i].empty();
	return ok;
}

// END C++
